Artificial Intelligence Elucidated with Examples

A renowned Stanford cardiologist who has pioneered cardiology research on global health illnesses, including atrial fibrillation, for over a decade, Sanjiv M. Narayan, MD, PhD, co-directed the electrophysiology service at the University of California and led the cardiac heart rhythm service at the University of California and Veterans Affairs Medical Center in San Diego for 13 years. Currently, a professor of medicine at Stanford University, Dr. Sanjiv Narayan, focuses on developing therapy that addresses global health issues and is interested in artificial intelligence (AI) -based digital healthcare solutions.

The notion that artificial intelligence is all about using computers to accomplish tasks that humans could only do has been the focus of many articles about this complex branch of digital health. Conversely, if a reader were to interpret the definition, it would mean, for example, that a regular business calculator is AI-embedded since simple arithmetic like addition and subtraction used to require human intelligence. On the contrary, a business calculator that collects manual inputs and processes such data does not connote what we know as artificial intelligence at present. To that extent, it may be worthwhile to elucidate the definition in a more applicable manner.

Artificial intelligence refers to the use of machines in processes that involve human intelligence. AI systems are designed to handle tasks that require human intelligence seamlessly. As used in this context, human intelligence refers to the ability to observe, learn, understand, and tackle new challenges through the practical application of what has been learnt. Consequently, human intelligence results in manipulating the environment by way of abstract thinking to keep on track toward specific objectives. Any machine that could demonstrate this type of intelligence is AI-embedded.

AI-embedded machines do not only mimic human intelligence. They add precision and efficiency to it. Of course, AI-embedded machines are built to facilitate processes, and to that extent, they have to accomplish designated tasks faster and better than a single human can. Google AI is a good example of this, as it can detect breast cancer 88 percent faster than human doctors.

Promising Breakthrough in Rheumatic Heart Disease Treatment

A skilled cardiologist with nearly three decades of medical experience, Sanjiv M. Narayan, MD, PhD, has pioneered innovative therapies for the treatment of atrial fibrillation and is presently a professor of medicine at Stanford University. Prior to this, he spent 13 years at the University of California, San Diego, as a professor of medicine, where he treated patients with heart rhythm disorders. Dr. Sanjiv Narayan is currently working on developing treatments that address global health issues and heart conditions.

One of the various heart diseases that has been receiving significant attention across the globe is rheumatic heart disease (RHD). Worldwide, rheumatic heart disease kills 300,000 people and subjects about 40 million people to discomfort each year. The condition, which is essentially caused by a bacterium called Streptococcus pyogenes, is totally preventable provided a patient has access to quality diagnostic tests and treatment. In developing countries (especially those in Africa), however, only a very small number of people have access to a standard level of care. Detecting RHD requires analyzing a patient’s EKG, which is expensive for some families.

To optimize diagnosis and also cut associated costs, a group of postgraduate researchers from accredited institutions, including the University of Manchester in the UK and the University of Cape Town in South Africa, teamed up to analyze hundreds of proteins that are biochemically implemented by RHD. They started the project by obtaining blood samples from critically ill patients in Africa. The proteins were extracted using an analytical technique called “proteomics.” Subsequently, analysis was conducted through machine learning (a type of artificial intelligence).

The researchers discovered that RHD resulted in certain inflammatory activities in all the 215 subjects. They also uncovered six proteins that can be integrated into inexpensive test kits’ technology as biomarker signatures. Potentially, this can mitigate the cost of electrocardiography during diagnosis since people can always rely on their cheap home test kits rather than spend money on tests for preventive reasons. Additionally, the findings suggest that prophylactic anti-inflammatory treatments can halt disease progression.

Digital Healthcare for Remote Patient Monitoring

With two decades of experience working in clinical and academic settings in locations such as Los Angeles, San Diego, and Cambridge, Sanjiv M. Narayan, MD, PhD, is a cardiologist and medical academic at Stanford University who develops novel therapies for unmet medical needs. Dr. Sanjiv Narayan takes an interest in healthcare-related technologies, such as digital healthcare.

By definition, digital health, also known as digital healthcare, encompasses a wide range of technologies that have been designed or repurposed for healthcare. For this reason, “digital health” is an interconnected and multidisciplinary concept that draws on various aspects of medicine and technology. Digital health encompasses telehealth and telemedicine, wearable devices, mobile health, electronic records, and more.

With digital health technologies designed for patient use, patients have more access to their health data through biosensor-embedded wearables. These devices can monitor blood sugar levels, blood pressure, and heart rate, among other variables. This way, patients have the opportunity to maintain constant follow-up on their health without always having to visit the clinic, and also make better educated decisions regarding their health through actionable insights from these devices. Some wearables are built with functions that keep health practitioners abreast of patients’ data so they can provide necessary medical intervention when required.

Understanding Big Data in Digital Healthcare

Since 2014, Sanjiv M. Narayan MD, PhD, has served as a professor of medicine at Stanford University with the goal of developing unique bioengineering innovations and therapies for doctors and cardiologists to treat complex clinical problems and improve therapy. Dr. Sanjiv Narayan formerly led the cardiac heart rhythm service at the University of California and Veterans Affairs Medical Center in San Diego, where he was able to develop a number of useful technologies. He is thus a major advocate for digital healthcare.

Digital healthcare is transforming the healthcare field with a combination of tools, technology, and the internet. This multidisciplinary concept uses a wide range of technology, including artificial intelligence and electronic health records, to advance preventative and personalized medicine. They boost treatment accuracy, optimize diagnosis, and also reduce the time lapse between diagnosis and treatment.

One of the key areas where the benefit of digital health is becoming obvious is big data. As the name implies, big data, with respect to healthcare, is all about collecting, analyzing, and making statistics-driven (ML-guided) decisions from ample clinical data that’s too enormous and complex for traditional data processing capacity. Rather, the data is analyzed and interpreted by a complex machine learning algorithm that has been trained to handle such analysis artificially. With time, the algorithm can learn the optimum ways of using the data to make accurate predictions.

One of the various ways big data is optimizing healthcare is through the actionable insights provided by ML algorithms. For instance, big data can help healthcare practitioners detect and make necessary corrections to medication errors on time. This is because the algorithm is complex enough to notice a lack of consistency between a patient’s health data and the medications they are given. If this is the case, the health practitioners can be notified of such errors, fostering quality care and optimizing treatment outcomes.

Digital Health’s Role In Neurology

Sanjiv M. Narayan, MD, PhD, is a professor of medicine at Stanford University in California. One of Sanjiv Narayan’s primary interests is the potential of digital health solutions, which could change the way doctors care for patients and develop new treatments.

Digital health refers to several digital solutions to administrative, diagnostic, and developmental problems in healthcare. In the early 2020s, these technologies have been explored to discover new ways of providing personalized care to patients. One trend that has stood out is the use of big data to automate personalized care, allowing doctors to parse a patient’s medical history more easily and needs to offer them the best treatment. Using this resource, doctors have been able to focus on providing preventative medicine due to their computer-enhanced prediction abilities.

One example of a field within medicine that digital solutions have enhanced is neuroscience. Famous for being in a convoluted area of study that deals with conditions as devastating as they are complex, neurologists often struggle to collect all the data they need to diagnose their patients. A neurologist, meeting a patient for the first time, might be limited in what data they can collect in just one appointment if the disease develops over months or years. Alternatively, a computer can interpret the scan results to project the patient’s history with their condition and possible future, potentially giving the doctor months’ worth of data in just one visit.